Coronary Heart Disease (CHD) remains the No.1 cause of death in the United States. The majority patients with CHD develop serious clinical symptoms, including myocardial infarction (MI), and the recurrence rate of MI becomes significantly increased after the initial attack. The accurate detection and quantification of myocardial scarring post-MI is critical for clinical diagnosis because it has been strongly associated with cardiac function, proven to be a predictive indicator for treatment prognosis, and is widely used as a biomarker for treatment evaluation. The current gold standard of MI scarring detection is to use magnetic resonance (MR) based Phase Sensitive Inversion Recovery (PSIR) technique, which can restore the polarity of the spin magnetization after inversion pulses. As 3D scans have been found to provide higher quantification and detection accuracy than 2D scans, the PSIR technique still faces two major challenges to fully adapt itself into a clinically viable D technique: 1) Low time-efficiency. Because the PSIR technique requires an extra fully sampled reference scan to restore the magnetization orientation, thus doubling scan time compared to non-PSIR LGE scans. As a result, although 3D PSIR can potentially provide improved detection and quantification accuracy, they have only been explored in research environments. 2) Poor contrast between the scar and bright blood pool. Although the PSIR reconstruction ensures strong scar/tissue contrast, the usually bright blood pool signal remains a confounding factor in quantifying scars, esp. for a subtle subendocardial scar. Our long-term goal is to develop an MR imaging technique as first-line diagnostic tool for myocardial scar detection/quantification in regular clinical practice. A newly designed RAPID reconstruction that does not require the 2nd acquisition for PS reconstruction will be used to improve the time efficiency and it will be combined with dedicated blood suppression techniques to improve the scar/blood contrast. The accuracy of the new techniques will also be validated in a clinical environment. By successfully achieving both aims, we expect to develop and validate a time-efficient clinically orientated 3D viability imaging technique that can significantly improve the current MI detection/quantification accuracy.